ArticleFrontiers in oncology2021
Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Review
- Genotoxicity of cancer therapies and the risk of secondary malignancies: toward personalized prevention.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- Crowdsourcing enables robust cell annotation for breast cancer pathology.Scientific reports · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Self-identified African ancestry and pathomics-derived tumor-infiltrating lymphocyte scores in colon and rectal adenocarcinoma whole-slide images: an exploratory multicohort whole-slide image study.Frontiers in oncology · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Deciphering site-specific histopathological parameters with potential clinical value in head and neck squamous cell carcinomas.Oncology letters · 2025Article
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
- A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology.Scientific reports · 2025Article
- Prognostic significance of CD8+ T cell Spatial Biomarkers in ER+ and ER- breast cancer: A retrospective cohort study.PLoS medicine · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Leveraging dysregulated tumor metabolism for targeting anticancer bacteria.Science advances · 2025Article
- Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system.Genome medicine · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- DDX54 downregulation enhances anti-PD1 therapy in immune-desert lung tumors with high tumor mutational burden.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The current landscape of artificial intelligence in computational histopathology for cancer diagnosis.Discover oncology · 2025Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as
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